Integrated decision making for attributes sampling and proactive maintenance in a discrete manufacturing system

Sinan Obaidat, Haitao Liao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

An integrated optimal design of attributes sampling and proactive maintenance for a discrete manufacturing system is studied in this paper. In the system, the failure of a critical component causes the process to shift. The new mathematical model for online sampling of the discrete manufacturing system is based on the binomial and truncated negative binomial distributions. In addition to performing scheduled maintenance and unscheduled corrective maintenance at the time of a true alarm, an additional maintenance opportunity when a false alarm occurs is also considered. The optimal scheduled maintenance time and sampling parameters are determined by solving a mixed integer nonlinear programming problem to minimise the long-run cost rate. A numerical example is provided to illustrate the proposed integrated attributes sampling and maintenance plan. The results show that the integrated approach outperforms the alternatives that consider different models separately. More importantly, showing the benefit of doing maintenance upon a false alarm provides a stakeholder with a new idea in managing a deteriorating manufacturing system.

Original languageEnglish (US)
Pages (from-to)5454-5476
Number of pages23
JournalInternational Journal of Production Research
Volume59
Issue number18
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • attributes sampling
  • integrated sampling and maintenance plan
  • Maintenance
  • multiple maintenance opportunities
  • truncated negative binomial distribution

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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